This invention relates generally to the field of analytical data processing, and more particularly to a multidimensional storage model and method for business intelligence portals and other data processing tools.
Business intelligence systems began largely as decision support systems (DSS) and executive information systems (EIS). Decision support systems (DSS) and executive information systems (EIS) were value added systems that provided additional information from existing on-line transactional processing (OLTP) systems.
As business intelligence systems developed, they integrated decision support system (DSS) functionality with executive information system (EIS) functionality, and added on-line analytical processing (OLAP) tools and management reporting tools. These hybrid business intelligence systems were gradually moved from a main-frame environment to a distributed server/desktop environment to allow greater user access.
More recently, the advent of centralized data warehouses and datamarts have created a dramatic increase in available data waiting to be analyzed, exploited and distributed within an organization. Such data warehouses and datamarts, however, were typically optimized for information delivery rather than transactional processing. As a result, data warehouses and datamarts offered only limited solutions for turning stored data into useful and strategic tactical information. During this same time, business intelligence systems gained prominence by offering sophisticated analysis tools for analyzing large amounts of stored information to support effective planning and decision making within an organization.
Such analysis tools provided by business intelligence systems include multidimensional data cubes that allow users to view and analyze intersections between data in different dimensions. A problem with traditional multidimensional data cubes, however, is that the size of the data cubes can increase very quickly, growing to ten or even hundreds of times the size of the original database. This is because the data cubes usually have a large number of intersections with no value. These empty intersections are stored to indicate that the corresponding data does not intersect. Another problem with traditional data cubes is that they are closed with all calculations being predefined, precalculated and stored as part of the data cube. As a result, if a user requests new calculations that were not anticipated during definition of a data cube, the whole cube must be recalculated and regenerated.
The present invention provides a multidimensional storage model and method that substantially eliminates or reduces disadvantages and problems associated with previous systems and methods. In particular, the multidimensional storage model utilizes a non-sparse architecture to minimize the size of model. In addition, the model uses an open architecture to allow calculations to be dynamically performed after the model is constructed.
In accordance with one embodiment of the present invention, a multidimensional storage model includes a set of non-sparse entries for each of a plurality of dimensions. The non-sparse entries each identify an associated data value. A set of interdimensional links is provided for each non-sparse entry. The interdimensional links each identify an intersection between non-sparse entries in disparate dimensions and collectively identify all intersections between non-sparse entries in the dimensions.
More specifically, in accordance with a particular embodiment of the present invention, the links are bi-directional and the non-sparse entries include a pointer to the associated data value. In this and other embodiments, the multidimensional storage model may include a set of calculated values for a calculated dimension. Each calculated value represents a value at an intersection between non-sparse entries in disparate dimensions.
Technical advantages of the present invention include providing an improved business intelligence portal that efficiently represents multidimensional data for analysis by a user. The multidimensional storage model provides interactive pivot views, data drilling for high and low level analysis and dynamic calculations of data intersections.
Another technical advantage of the present invention includes providing an efficient multidimensional storage model. In particular, the multidimensional storage model utilizes a non-sparse architecture to minimize system resources necessary to generate and store the model. The reduced size of the model improves processing times and allows efficient pivot and drill operations during data analysis.
Still another technical advantage of the present invention includes providing an improved multidimensional storage model having an open architecture. In particular, the multidimensional storage model allows additional calculations to be performed after the model is constructed. As a result, a user can create new calculations to analyze data intersections that were not anticipated during the original definition of the model. This reduces time and resources needed to support pivot and drill operations.